Method and apparatus for measuring and improving efficiency in refrigeration systems

ABSTRACT

An apparatus for optimizing an efficiency of a refrigeration system, comprising means for measuring a refrigeration efficiency of an operating refrigeration system; means for altering a process variable of the refrigeration system during efficiency measurement; and a processor for calculating a process variable level which achieves an optimum efficiency. The process variables may include refrigerant charge and refrigerant oil concentration in evaporator.

CROSS REFERENCE TO RELATED APPLICATIONS

The present application is a continuation of U.S. patent applicationSer. No. 14/980,962, filed Dec. 28, 2015, now U.S. Pat. No. 10,041,713,issued Aug. 7, 2018, which is a continuation of U.S. patent applicationSer. No. 13/707,829, filed Dec. 7, 2012, issued as U.S. Pat. No.9,222,712 on Dec. 29, 2015, which is a continuation of U.S. patentapplication Ser. No. 12/898,289, filed Oct. 5, 2010, issued as U.S. Pat.No. 8,327,653 on Dec. 11, 2012, which is a continuation of U.S. patentapplication Ser. No. 12/468,506, filed on May 19, 2009, issued as U.S.Pat. No. 7,805,952 on Oct. 5, 2010, which is a continuation of U.S.patent application Ser. No. 11/463,101, filed Aug. 8, 2006, issued asU.S. Pat. No. 7,533,536 on May 19, 2009, which is a continuation of U.S.patent application Ser. No. 11/182,249, filed Jul. 14, 2005, issued asU.S. Pat. No. 7,086,240 on Aug. 8, 2006, which is a continuation of U.S.patent application Ser. No. 10/338,941, filed Jan. 8, 2003, issued asU.S. Pat. No. 7,059,143 on Jun. 13, 2006, which is acontinuation-in-part of U.S. patent application Ser. No. 09/577,703filed May 23, 2000, issued as U.S. Pat. No. 6,505,475 on Jan. 14, 2003,which claims benefit of priority from U.S. Provisional PatentApplication 60/150,152 filed Aug. 20, 1999, and U.S. Provisional PatentApplication 60/174,993 filed Jan. 7, 2000, each of which is expresslyincorporated herein by reference in its entirety.

FIELD OF THE INVENTION

The present invention relates to the fields of repair, maintenance andtuning of refrigeration systems, and more particularly for systems andmethods for measuring, analyzing and improving the efficiency ofrefrigeration systems.

BACKGROUND OF THE INVENTION

In the field of refrigeration and chillers, the evaporator heatexchanger is a large structure, containing a plurality of paralleltubes, within a larger vessel comprising a shell, through whichrefrigerant flows, absorbing heat and evaporating. Outside the tubes, anaqueous medium, such as brine, circulates and is cooled, which is thenpumped to the process region to be cooled. Such an evaporator may holdhundreds or thousands of gallons of aqueous medium with an even largercirculating volume.

Mechanical refrigeration systems are well known. Their applicationsinclude refrigeration, heat pumps, and air conditioners used both invehicles and in buildings. The vast majority of mechanical refrigerationsystems operate according to similar, well known principles, employing aclosed-loop fluid circuit through which refrigerant flows, with a sourceof mechanical energy, typically a compressor, providing the motiveforces.

Typical refrigerants are substances that have a boiling point below thedesired cooling temperature, and therefore absorb heat from theenvironment while evaporating under operational conditions. Thus, theenvironment is cooled, while heat is transferred to another locationwhere the latent heat of vaporization is shed. Refrigerants thus absorbheat via evaporation from one area and reject it via condensation intoanother area. In many types of systems, a desirable refrigerant providesan evaporator pressure as high as possible and, simultaneously, acondenser pressure as low as possible. High evaporator pressures implyhigh vapor densities, and thus a greater system heat transfer capacityfor a given compressor. However, the efficiency at the higher pressuresis lower, especially as the condenser pressure approaches the criticalpressure of the refrigerant. It has generally been found that themaximum efficiency of a theoretical vapor compression cycle is achievedby fluids with low vapor heat capacity, associated with fluids withsimple molecular structure and low molecular weight.

Refrigerants must satisfy a number of other requirements as best aspossible including: compatibility with compressor lubricants and thematerials of construction of refrigerating equipment, toxicity,environmental effects, cost availability, and safety.

The fluid refrigerants commonly used today typically include halogenatedand partially halogenated alkanes, including chlorofluorocarbons (CFCs),hydrochlorofluorocarbons (HFCFs), and less commonly hydrofluorocarbons(HFCs) and perfluorocarbons (PFCs). A number of other refrigerants areknown, including propane and fluorocarbon ethers. Some commonrefrigerants are identified as R11, R12, R22, R500, and R502, eachrefrigerant having characteristics that make them suitable for differenttypes of applications.

A refrigeration system typically includes a compressor, which compressesgaseous refrigerant to a relatively high pressure, while simultaneouslyheating the gas, a condenser, which sheds the heat from the compressedgas, allowing it to condense into a liquid phase, and an evaporator, inwhich the liquefied refrigerant is again vaporized, withdrawing the heatof vaporization from a process. The compressor therefore provides themotive force for active heat pumping from the evaporator to thecondenser. The compressor typically requires a lubricant, in order toprovide extended life and permit operation with close mechanicaltolerances. Normally, the gaseous refrigerant and liquid lubricant areseparated by gravity, so that the condenser remains relatively oil free.However, over time, lubricating oil migrates out of the compressor andits lubricating oil recycling system, into the condenser. Once in thecondenser, the lubricating oil becomes mixed with the liquefiedrefrigerant and is carried to the evaporator. Since the evaporatorevaporates the refrigerant, the lubricating oil accumulates at thebottom of the evaporator. The oil in the evaporator tends to bubble, andforms a film on the walls of the evaporator tubes. In some cases, suchas fin tube evaporators, a small amount of oil enhances heat transferand is therefore beneficial. In other cases, such as nucleation boilingevaporator tubes, the presence of oil, for example over 1%, results inreduced heat transfer. See, Schlager, L. M., Pate, M. B., and Berges, A.E., “A Comparison of 150 and 300 SUS Oil Effects on RefrigerantEvaporation and Condensation in a Smooth Tube and Micro-fin Tube”,ASHRAE Trans. 1989, 95(1):387-97; Thome, J. R., “ComprehensiveThermodynamic Approach to Modelling Refrigerant-Lubricating OilMixtures”, Intl. J. HVAC&R Research (ASHRAE) 1995, 110-126; Poz, M. Y.,“Heat Exchanger Analysis for Nonazeotropic Refrigerant Mixtures”, ASHRAETrans. 1994, 100(1)727-735 (Paper No. 95-5-1).

Several mechanisms are available seeking to control lubricating oilbuildup in the evaporator. One mechanism provides a shunt for a portionof mixed liquid refrigerant and oil in the evaporator to the compressor,wherein the oil is subject to the normal recycling mechanisms. Thisshunt, however, may be inefficient and is difficult to control. Further,it is difficult to achieve and maintain low oil concentrations usingthis method.

It is also known to periodically purge the system, recycling therefrigerant with purified refrigerant and cleaning the system. Thistechnique, however, generally permits rather large variance in systemefficiency or relatively high maintenance costs. Further, this techniquegenerally does not acknowledge that there is an optimum level of oil inthe evaporator and, for example, the condenser. Thus, typicalmaintenance seeks to produce a “clean” system, subject to incrementalchanges after servicing.

It is thus known that the buildup of large quantities of refrigerant oilwithin an evaporator, which passes in small amounts from the compressorto the condenser as a gas, and which leaves the condenser and passes tothe evaporator as a liquid, will reduce efficiency of the system, andfurther, it is known to provide in-line devices which continuouslyremove refrigerant oil from the refrigerant entering the evaporator.These devices include so-called oil eductors.

The inefficiency of these continuous removal devices is typically as aresult of the bypassing of the evaporator by a portion of therefrigerant, and potentially a heat source to vaporize or partiallydistill the refrigerant to separate the oil. Therefore, only a smallproportion of the refrigerant leaving the condenser may be subjected tothis process, resulting in poor control of oil level in the evaporatorand efficiency loss.

It is also known to reclaim and recycle refrigerant from a refrigerationsystem to separate oil and provide clean refrigerant. This process istypically performed manually and requires system shutdown.

Systems are available for measuring the efficiency of a chiller, i.e., arefrigeration system which cools water or a water solution, such asbrine. In these systems, the efficiency is calculated based onWatt-hours of energy consumed (Volts×Amps×hours) per cooling unit,typically tons or British Thermal Unit (BTU) (the amount of energyrequired to change the temperature of one British ton of water 1° C.

Thus, a minimal measurement requires a clock, voltmeter, ammeter, andthermometers and flowmeters for the inlet and outlet water. Typically,further instruments are provided, including a chiller water pressuregage, gages for the pressure and temperature of evaporator andcondenser. A data acquisition system is also typically provided tocalculate the efficiency, in BTU/kWH.

The art, however, does not provide systems intended to measure theoperating efficiency of commercial chillers, while permittingoptimization of the system.

It is known that the charge conditions of a chiller may have asubstantial effect on both system capacity and system operatingefficiency. Simply, the level of refrigerant charge in a chillercondenser directly relates to the cooling capacity of the system, allother things being equal. Thus, in order to handle a larger heat load, agreater quantity of refrigerant is required. However, by providing thislarge refrigerant charge, the operating efficiency of the system atreduced loads is reduced, thus requiring more energy for the same BTUcooling. Bailey, Margaret B., “System Performance Characteristics of aHelical Rotary Screw Air-Cooled Chiller Operating Over a Range ofRefrigerant Charge Conditions”, ASHRAE Trans. 1998 104(2), expresslyincorporated herein by reference. Therefore, by correctly selecting the“size” (e.g., cooling capacity) of the chiller, efficiency is enhanced.However, typically the chiller capacity is determined by the maximumexpected design load, and thus for any given design load, the quantityof refrigerant charge is dictated. Therefore, in order to achieveimproved system efficiency, a technique of modulation recruitment isemployed, in which one or more of a plurality of subsystems areselectively activated depending on the load, to allow efficient designof each subsystem while permitting a high overall system load capacitywith all subsystems operational. See, Trane “Engineer's Newsletter”December 1996, 25(5):1-5. Another known technique seeks to alter therotational speed of the compressor. See, U.S. Pat. No. 5,651,264,expressly incorporated herein by reference.

Chiller efficiency generally increases with chiller load. Thus, anoptimal system seeks to operate system near its rated design. Higherrefrigerant charge level, however, results in deceased efficiency.Further, chiller load capacity sets a limit on the minimum refrigerantcharge level. Therefore, it is seen that there exists an optimumrefrigerant charge level for maximum efficiency.

Chiller efficiency depends on several factors, including subcoolingtemperature and condensing pressure, which, in turn, depend on the levelof refrigerant charge, nominal chiller load, and the outdoor airtemperature. First, subcooling within the thermodynamic cycle will beexamined. FIG. 6A shows a vapor compression cycle schematic and FIG. 6Bshows an actual temperature-entropy diagram, wherein the dashed lineindicates an ideal cycle. Upon exiting the compressor at state 2, asindicated in FIG. 6A, a high-pressure mixture of hot gas and oil passesthrough an oil separator before entering the tubes of the remoteair-cooled condenser where the refrigerant rejects heat (Qh) to movingair by forced convection. In the last several rows of condenser coils,the high-pressure saturated liquid refrigerant should be subcooled,e.g., 10 F. to 20 F (5.6 C to 11.1 C), according to manufacturer'srecommendations, as shown by state 3 in FIG. 6B. This level ofsubcooling allows the device following the condenser, the electronicexpansion valve, to operate properly. In addition, the level ofsubcooling has a direct relationship with chiller capacity. A reducedlevel of subcooling results in a shift of state 3 (in FIG. 6B) to theright and a corresponding shift of state 4 to the right, therebyreducing the heat removal capacity of the evaporator (Q1).

As the chiller's refrigerant charge increases, the accumulation ofrefrigerant stored in the condenser on the high-pressure side of thesystem also increases. An increase in the amount of refrigerant in thecondenser also occurs as the load on the chiller decreases due to lessrefrigerant flow through the evaporator, which results in increasedstorage in the condenser. A flooded condenser causes an increase in theamount of sensible heat transfer area used for subcooling and acorresponding decrease in the surface area used for latent or isothermalheat transfer associated with condensing. Therefore, increasingrefrigerant charge level and decreasing chiller load both result inincreased subcooling temperatures and condensing temperatures.

Increased outdoor air temperatures have an opposite effect on theoperation of the condenser. As the outdoor air temperature increases,more condenser surface area is used for latent or isothermal heattransfer associated with condensing and a corresponding decrease insensible heat transfer area used for subcooling. Therefore, increases inoutdoor air temperature result in decreased subcooling temperatures andincreased condensing temperatures.

Referring to FIG. 6B, an increase in subcooling drives state 3 to theleft, while an increase in condensing temperature shifts the curveconnecting states 2 and 3 upward. High condensing temperatures canultimately lead to compressor motor overload and increased compressorpower consumption or lowered efficiency. As subcooling increases, heatis added to the evaporator, resulting in an upward shift of the curveconnecting states 4 and 1. As the evaporating temperature increases, thespecific volume of the refrigerant entering the compressor alsoincreases, resulting in increased power input to the compressor.Therefore, increased levels of refrigerant charge and decreased chillerload conditions result in increased subcooling, which leads to increasedcompressor power input.

Control of the electronic expansion valve is based on a sensor locatedwithin the compressor's inlet where it measures superheat level.Superheat level is represented by the slight increase in temperatureafter the refrigerant leaves the saturation curve, as shown at state 1in FIG. 6B. Vaporized refrigerant leaves the chiller's evaporator andenters the compressor as a superheated vapor with a recommended setpoint(2.2 C) superheat to avoid premature failure from droplet pitting anderosion.

As discussed previously, an increase in outdoor air temperature causesan increase in discharge pressure, which, in turn, causes thecompressor's suction pressure to increase. The curves connecting states2 and 3 and states 4 and 1 on FIG. 6B 3 both shift upward due toincreases in outdoor air temperature. An upward shift in curves 4through 1 or an increase in refrigerant evaporating temperature resultsin a decrease in the evaporating approach temperature. As the approachtemperature decreases, the mass flow rate through the evaporator mustincrease in order to remove the proper amount of heat from the chilledwater loop. Therefore, increasing outdoor air temperatures causeevaporating pressure to increase, which leads to increased refrigerantmass flow rate through the evaporator. The combined effect of higherrefrigerant mass flow rate through the evaporator and reduced approachtemperature causes a decrease in superheat temperatures. Therefore, aninverse relationship exists between outdoor air temperature andsuperheat temperatures.

With decreasing refrigerant charge, the curve connecting states 2 and 3in FIG. 6B shifts downward and the subcooling level decreases or state 3on the T-s diagram in FIG. 6B moves to the right. Bubbles begin toappear in the liquid line leading to the expansion device due to anincreased amount of gaseous refrigerant leaving the condenser. Withoutthe proper amount of subcooling in the refrigerant entering theexpansion device (state 3 in FIG. 6B), the device does not operateoptimally. In addition, a decrease in refrigerant charge causes adecrease in the amount of liquid refrigerant that flows into theevaporator and a subsequent decrease in capacity and increase insuperheat and suction pressure. Thus, an inverse relationship existsbetween refrigerant charge level and superheat temperature.

Under extreme refrigerant undercharge conditions (below −20% charge),refrigerant undercharge causes an increase in suction pressure. Ingeneral, the average suction pressure increases with increasingrefrigerant charge during all charge levels above −20%. Refrigerantcharge level is a significant variable in determining both superheattemperature and suction pressure.

U.S. Pat. Nos. 4,437,322; 4,858,681; 5,653,282; 4,539,940; 4,972,805;4,382,467; 4,365,487; 5,479,783; 4,244,749; 4,750,547; 4,645,542;5,031,410; 5,692,381; 4,071,078; 4,033,407; 5,190,664; and 4,747,449relate to heat exchangers and the like.

There are a number of known methods and apparatus for separatingrefrigerants, including U.S. Pat. Nos. 2,951,349; 4,939,905; 5,089,033;5,110,364; 5,199,962; 5,200,431; 5,205,843; 5,269,155; 5,347,822;5,374,300; 5,425,242; 5,444,171; 5,446,216; 5,456,841; 5,470,442;5,534,151; and 5,749,245. In addition, there are a number of knownrefrigerant recovery systems, including U.S. Pat. Nos. 5,032,148;5,044,166; 5,167,126; 5,176,008; 5,189,889; 5,195,333; 5,205,843;5,222,369; 5,226,300; 5,231,980; 5,243,831; 5,245,840; 5,263,331;5,272,882; 5,277,032; 5,313,808; 5,327,735; 5,347,822; 5,353,603;5,359,859; 5,363,662; 5,371,019; 5,379,607; 5,390,503; 5,442,930;5,456,841; 5,470,442; 5,497,627; 5,502,974; 5,514,595; and 5,934,091.Also known are refrigerant property analyzing systems, as shown in U.S.Pat. Nos. 5,371,019; 5,469,714; and 5,514,595.

SUMMARY OF THE INVENTION

The present invention provides a system and method measuring, analyzingand manipulating the efficiency of a refrigeration system byinstrumenting the refrigeration system to measure efficiency, selectinga process variable for manipulation, and altering the process variableduring operation of the refrigeration system while measuring efficiencythereof. Preferably, the efficiency is recorded in conjunction with theprocess variables. Thus, for each system, the actual sensitivity ofefficiency, detected directly or by surrogate measures, to a processvariable, may be measured.

According to the present invention, multivariate optimization andcontrol may be conducted. In the case of multivariate analysis andcontrol, interaction between variables or complex sets of time-constantsmay require a complex control system. A number of types of control maybe implemented to optimize the operation of the system.

Typically, after the appropriate type of control is selected, it must betuned to the system, thus defining efficient operation and the relationof the input variables from sensors on the efficiency of the system.Often, controls often account for time delays inherent in the system,for example to avoid undesirable oscillation or instability. In manyinstances, simplifying presumptions, or segmentations are made inanalyzing the operating space to provide traditional analytic solutionsto the control problems. In other instances, non-linear techniques areemployed to analyze the entire range of input variables. Finally, hybridtechniques are employed using both non-linear techniques and simplifyingpresumptions or segmentation of the operating space.

For example, in one embodiment of the invention, it is preferred thatthe range of operating conditions be segmented along orthogonaldelineations, and the sensitivity of the system to process variablemanipulation be measured for each respective variable within a segment.This, for example, permits a monotonic change in each variable during atesting or training phase, rather than requiring both increasing anddecreasing respective variables in order to map the entire operatingspace. On the other hand, in the case of a single variable, it ispreferred that the variable be altered continuously while measurementsare taking place in order to provide a high speed of measurement.

It is also possible to employ a so-called adaptive control system, inwhich the sensitivity of the operating efficiency to small perturbationsin the control variables are measured during actual operation of thesystem, rather than in a testing or training mode, as in an autotuningsystem, which may be difficult to arrange and which may be inaccurate orincomplete if the system configuration or characteristics change aftertraining or testing. Manual tuning is typically not feasible since thecharacteristics of each installation over the entire operating range arenot often fully characterized and are subject to change over time.

Manual tuning methods thus require an operator to run different test ortrial and error procedures to determine the appropriate controlparameters. Some manual tuning methods are described in D. E. Seborg, T.F. Edgar, and D. A. Mellichamp, Process Dynamics and Control, John Wiley& Sons, New York (1989) and A. B. Corripio, Tuning of Industrial ControlSystems, Instrument Society of America, Research Triangle Park, N.C.(1990). Manual tuning methods have the obvious disadvantage of requiringlarge amounts of operator time and expertise, although the use ofpractical expertise may often result in an acceptable control strategyusing a relatively simple controller.

Autotuning methods require an operator to periodically initiate tuningprocedures, during which the controller will automatically determine theappropriate control parameters. The control parameters thus set willremain unchanged until the next tuning procedure. Some autotuningprocedures are described in K. J. Astrom and T. Hagglund, AutomaticTuning of PID Controllers, Instrument Society of America, ResearchTriangle Park, N.C. (1988). While autotuning requires less operator timethan manual tuning methods, it still requires operator intervention, andfurther requires an interruption of normal system operation.

With adaptive control methods, the control parameters are automaticallyadjusted during normal operation to adapt to changes in processdynamics. Thus, no operator intervention is required. Further, thecontrol parameters are continuously updated to prevent the degradedperformance which may occur between the tunings of the other methods. Onthe other hand, adaptive control methods may result in inefficiency dueto the necessary periodic variance from an “optimal” condition in orderto test the optimality. Further, adaptive controls may be complex andrequire a high degree of intelligence. Numerous adaptive control methodshave been developed. See, for example, C. J. Harris and S. A. Billings,Self-Tuning and Adaptive Control: Theory and Applications, PeterPeregrinus LTD (1981). There are three main approaches to adaptivecontrol: model reference adaptive control (“MRAC”), self-tuning control,and pattern recognition adaptive control (“PRAC”). The first twoapproaches, MRAC and self-tuning, rely on system models which aregenerally quite complex. The complexity of the models is necessitated bythe need to anticipate unusual or abnormal operating conditions.Specifically, MRAC involves adjusting the control parameters until theresponse of the system to a command signal follows the response of areference model. Self-tuning control involves determining the parametersof a process model on-line and adjusting the control parameters basedupon the parameters of the process model. Methods for performing MRACand self-tuning control are described in K. J. Astrom and B. Wittenmark,Adaptive Control, Addison-Wesley Publishing Company (1989). Inindustrial chillers, adequate models of the system are typicallyunavailable for implementing the control, so that self-tuning controlsare preferred over traditional MRAC.

With PRAC, parameters that characterize the pattern of the closed-loopresponse are determined after significant setpoint changes or loaddisturbances. The control parameters are then adjusted based upon thecharacteristic parameters of the closed-loop response. A patternrecognition adaptive controller known as EXACT is described by T. W.Kraus and T. J. Myron, “Self-Tuning PID Controller uses PatternRecognition Approach,” Control Engineering, pp. 106-111, June 1984, E.H. Bristol and T. W. Kraus, “Life with Pattern Adaptation,” Proceedings1984 American Control Conference, pp. 888-892, San Diego, Calif. (1984),and K. J. Astrom and T. Hagglund, Automatic Tuning of PID Controllers,Instrument Society of America, Research Triangle Park, N.C. (1988). Seealso U.S. Pat. No. Re. 33,267, expressly incorporated herein byreference. The EXACT method, like other adaptive control methods, doesnot require operator intervention to adjust the control parameters undernormal operation. Before normal operation may begin, EXACT requires acarefully supervised startup and testing period. During this period, anengineer must determine the optimal initial values for controller gain,integral time, and derivative time. The engineer must also determine theanticipated noise band and maximum wait time of the process. The noiseband is a value representative of the expected amplitude of noise on thefeedback signal. The maximum wait time is the maximum time the EXACTalgorithm will wait for a second peak in the feedback signal afterdetecting a first peak. Further, before an EXACT-based controller is putinto normal use, the operator may also specify other parameters, such asthe maximum damping factor, the maximum overshoot, the parameter changelimit, the derivative factor, and the step size. In fact, the provisionof these parameters by an expert engineer is generally appropriate inthe installation process for a control of an industrial chiller, andtherefore such a manual definition of initial operating points ispreferred over techniques which commence without a priori assumptions.

In the EXACT method, the value of the parameter change limit, which maybe supplied as a predetermined constant or entered by a user, defines arange within which the parameter values of the controller are consideredvalid. For example, the EXACT method will not set the proportional gainof a controller to a value that exceeds the upper limit of the rangedefined by the parameter change limit. By specifying a valid parameterrange, the EXACT method prevents the controller from using the extremeparameter values that may be a result of hardware or software errors ordeficiencies. However, by constraining the parameters to values thatfall within a designated range, the EXACT method prevents the use ofparameter values outside the range even when such values would provideimproved performance; likewise, this constraint fails to detecterroneous or artifact sensor data within the parameter change limit.Thus, an improvement over this technique provides an intelligentanalysis of sensor data to perform an automated fault detection andanalysis. Thus, using a model of the system constructed duringoperation, possibly along with manual input of probable normaloperational limits, the system may analyze sensor data to determine aprobability of system malfunction. If the probability exceeds athreshold, an error may be indicated or other remedial action taken.

A second known pattern recognition adaptive controller is described byChuck Rohrer and Clay G. Nesler in “Self-Tuning Using a PatternRecognition Approach,” Johnson Controls, Inc., Research Brief 228 (Jun.13, 1986). The Rohrer controller calculates the optimal controlparameters based on a damping factor, which in turn is determined by theslopes of the feedback signal. Similar to EXACT, the Rohrer methodrequires an engineer to enter a variety of initial values before normaloperation may commence. Specifically, an operator must specify theinitial values for a proportional band, an integral time, a deadband, atune noise band, a tune change factor, an input filter, and an outputfilter. This system thus emphasizes temporal control parameters.

Manual tuning of loops can take a long time, especially for processeswith slow dynamics, including industrial and commercial chillers.Different methods for autotuning PID controllers are described byAstrom, K. J., and T. Hagglund, Automatic Tuning of PID Controllers,Instrument Society of American, Research Triangle Park, N.C., 1988, andSeborg, D. E. T., T. F. Edgar, and D. A. Mellichamp, Process Dynamicsand Control, John Wiley & sons, 1989. Several methods are based on theopen loop transient response to a step change in controller output andother methods are based on the frequency response while under some formof feedback control. Open loop step response methods are sensitive toload disturbances, and frequency response methods require a large amountof time to tune systems with long time constants. The Ziegler-Nicholstransient response method characterizes the response to a step change incontroller output, however, implementation of this method is sensitiveto noise. See also, Nishikawa, Yoshikazu, Nobuo Sannomiya, Tokuji Ohta,and Haruki Tanaka, “A Method for Autotuning of PID Control Parameters,”Automatica, Volume 20, No. 3, 1984.

For some systems, it is often difficult to determine if a process hasreached a steady-state. In many systems, if the test is stopped tooearly, the time delay and time constant estimates may be significantlydifferent than the actual values. For example, if a test is stoppedafter three time constants of the first order response, then theestimated time constant equals 78% of the actual time constant, and ifthe test is stopped after two time constants, then the estimated timeconstant equals 60% of the actual time constant. Thus, it is importantto analyze the system in such a way as to accurately determinetime-constants.

Thus, in a self-tuning system, the algorithm may obtain tuning data fromnormal perturbations of the system, or by periodically testing thesensitivity of the plant to modest perturbations about the operatingpoint of the controlled variable(s). If the system determines that theoperating point is inefficient, the controlled variable(s) are alteredin order to improve efficiency toward an optimal operating point. Theefficiency may be determined on an absolute basis, such as by measuringkWatt hours consumed per BTU of cooling, or through surrogatemeasurements of energy consumption or cooling, such as temperaturedifferentials and flow data of refrigerant near the compressor and/orwater in the secondary loop near the evaporator/heat exchanger. Thus, afull power management system (PMS) is not required in order to optimizethe efficiency.

In many instances, parameters will vary linearly with load and beindependent of other variables, thus simplifying analysis and permittingtraditional (e.g., linear, proportional-integral-differential (PID))control design. See, U.S. Pat. Nos. 5,568,377, 5,506,768, and 5,355,305,expressly incorporated herein by reference. On the other hand,parameters will have multifactorial dependencies which are not easilyresolved. In this case, a neural network or fuzzy-neural network controlis preferred. In order to train a neural network, two options areavailable. First, a specific training mode may be provided, in which theoperating conditions are varied, generally methodically, over the entireoperating space, by imposing artificial or controlled loads andextrinsic parameters on the system. Thereafter, the neural network istrained, for example by back propagation of errors, to produce an outputthat moves the system toward an optimal operating point for the actualload conditions. The controlled variables may be, for example, oilconcentration in the refrigerant and/or refrigerant charge. See, U.S.Pat. No. 5,579,993, expressly incorporated herein by reference.

Second, the system operates in a continual learning mode in which thelocal operating space of the system is mapped by the control duringoperation, in order to determine a sensitivity of the system toperturbations in controlled process variables, such as oil concentrationin the refrigerant and/or refrigerant charge. When the system determinesthat the present operating point is suboptimal, it alters the operatingpoint toward a presumable more efficient condition. If the process hasinsufficient variability to adequately map the operating point, thecontrol algorithm may conduct a methodical search of the space or injecta pseudorandom signal into one or more controlled variables seeking todetect the effect on the output (efficiency). Generally, such searchtechniques will themselves have only a small effect on systemefficiency, and will allow the system to learn new conditions, withoutexplicitly entering a learning mode after each alteration in the system.

Preferably, the control builds a map or model of the operating spacefrom experience, and, when the actual system performance corresponds tothe map or model, uses this map or model to predict an optimal operatingpoint and directly control the system to achieve the predictedmost-efficient state. On the other hand, when the actual performancedoes not correspond to the map or model, the control seeks to generate anew map or model.

See, U.S. Pat. No. 5,506,768, expressly incorporated herein byreference. See, also:

-   A. B. Corripio, “Tuning of Industrial Control Systems”, Instrument    Society of America, Research Triangle Park, N.C. (1990) pp. 65-81.-   C. J. Harris & S. A. Billings, “Self-Tuning and Adaptive Control:    Theory and Applications”, Peter Peregrinus LTD (1981) pp. 20-33.-   C. Rohrer & Clay Nesler, “Self-Tuning Using a Pattern Recognition    Approach”, Johnson Controls, Inc., Research Brief 228 (Jun. 13,    1986).-   D. E. Seborg, T. F. Edgar, & D. A. Mellichamp, “Process Dynamics and    Control”, John Wiley & Sons, NY (1989) pp. 294-307, 538-541.-   E. H. Bristol & T. W. Kraus, “Life with Pattern Adaptation”,    Proceedings 1984 American Control Conference, pp. 888-892, San    Diego, Calif. (1984).-   Francis Schied, “Shaum's Outline Series-Theory & Problems of    Numerical Analysis”, McGraw-Hill Book Co., NY (1968) pp. 236, 237,    243, 244, 261.-   K. J. Astrom and B. Wittenmark, “Adaptive Control”, Addison-Wesley    Publishing Company (1989) pp. 105-215.-   K. J. Astrom, T. Hagglund, “Automatic Tuning of PID Controllers”,    Instrument Society of America, Research Triangle Park, N.C. (1988)    pp. 105-132.-   R. W. Haines, “HVAC Systems Design Handbook”, TAB Professional and    Reference Books, Blue Ridge Summit, Pa. (1988) pp. 170-177.-   S. M. Pandit & S. M. Wu, “Timer Series & System Analysis with    Applications”, John Wiley & Sons, Inc., NY (1983) pp. 200-205.-   T. W. Kraus 7 T. J. Myron, “Self-Tuning PID Controller Uses Pattern    Recognition Approach”, Control Engineering, pp. 106-111, June 1984.-   G F Page, J B Gomm & D Williams: “Application of Neural Networks to    Modelling and Control”, Chapman & Hall, London, 1993.-   Gene F Franklin, J David Powell & Abbas Emami-Naeini: “Feedback    Control of Dynamic Systems”, Addison-Wesley Publishing Co. Reading,    1994.-   George E P Box & Gwilym M Jenkins: “Time Series Analysis:    Forecasting and Control”, Holden Day, San Francisco, 1976.-   Sheldon G Lloyd & Gerald D Anderson: “Industrial Process Control”,    Fisher Controls Co., Marshalltown, 1971.-   Kortegaard, B. L., “PAC-MAN, a Precision Alignment Control System    for Multiple Laser Beams Self-Adaptive Through the Use of Noise”,    Los Alamos National Laboratory, date unknown.-   Kortegaard, B. L., “Superfine Laser Position Control Using    Statistically Enhanced Resolution in Real Time”, Los Alamos National    Laboratory, SPIE-Los Angeles Technical Symposium, Jan. 23-25, 1985.-   Donald Specht, IEEE Transactions on Neural Networks, “A General    Regression Neural Network”, November 1991, Vol. 2, No. 6, pp.    568-576.

Fuzzy controllers may be trained in much the same way neural networksare trained, using backpropagation techniques, orthogonal least squares,table look-up schemes, and nearest neighborhood clustering. See Wang,L., Adaptive fuzzy systems and control, New Jersey: Prentice-Hall(1994); Fu-Chuang Chen, “Back-Propagation Neural Networks for NonlinearSelf-Tuning Adaptive Control”, 1990 IEEE Control System Magazine.

Thus, while a system model may be useful, especially for large changesin system operating parameters, the adaptation mechanism is advantageousin that it does not rely on an explicit system model, unlike many of theon-line adaptation mechanisms such as those based on Lyapunov methods.See Wang, 1994; Kang, H. and Vachtsevanos, G., “Adaptive fuzzy logiccontrol,” IEEE International Conference on Fuzzy Systems, San Diego,Calif. (March 1992); Layne, J., Passino, K. and Yurkovich, S., “Fuzzylearning control for antiskid braking systems,” IEEE Transactions onControl Systems Technology 1 (2), pp. 122-129 (1993).

The adaptive fuzzy controller (AFC) is a nonlinear, multiple-inputmultiple-output (MIMO) controller that couples a fuzzy control algorithmwith an adaptation mechanism to continuously improve system performance.The adaptation mechanism modifies the location of the output membershipfunctions in response to the performance of the system. The adaptationmechanism can be used off-line, on-line, or a combination of both. TheAFC can be used as a feedback controller, which acts using measuredprocess outputs and a reference trajectory, or as a feedback controllerwith feedforward compensation, which acts using not only measuredprocess outputs and a reference trajectory but also measureddisturbances and other system parameters. See, U.S. Pat. Nos. 5,822,740,5,740,324, expressly incorporated herein by reference.

Preferably, a particular process variable is the oil content of therefrigerant in the evaporator. To define the control algorithm, theprocess variable, e.g., oil content, is continuously varied by partiallydistilling the refrigerant at or entering the evaporator to remove oil,providing clean refrigerant to the evaporator in an autotuningprocedure. Over time, the oil content will approach zero. Through thismethod, the optimal oil content in the evaporator and the sensitivity tochanges in oil content can be determined. In a typical installation, theoptimum oil concentration in the evaporator is near 0%, while when thesystem is retrofitted with a control system for controlling the oilcontent of the evaporator, it is well above optimum. Therefore, theautotuning of the control may occur simultaneously with the remediationof the inefficiency.

In fact, the oil content of the evaporator may be independentlycontrolled, or controlled in concert with other variables, such asrefrigerant charge. In this case, an external reservoir or refrigerantis provided. Refrigerant is withdrawn from the evaporator through apartial distillation apparatus into the reservoir, with the oilseparately stored. Based on the control optimization, refrigerant andoil are separately returned to the system, i.e., refrigerant vapor tothe evaporator and oil to the compressor loop. In this way, the optimumoil concentration may be maintained for respective refrigerant chargelevels. It is noted that this system is generally asymmetric; withdrawaland partial distillation of refrigerant is relatively slow, whilecharging the system with refrigerant and oil are relatively quick. Ifrapid withdrawal of refrigerant is desired, the partial distillationsystem may be temporarily bypassed. However, typically it is moreimportant to meet peak loads quickly than to obtain most efficientoperating parameters subsequent to peak loads.

The optimal refrigerant charge level may be subject to variation withnominal chiller load and plant temperature, while related (dependent)variables include efficiency (kW/ton), superheat temperature, subcoolingtemperature, discharge pressure, superheat temperature, suction pressureand chilled water supply temperature percent error. Typically, thedirect efficiency measurement of kilowatt-hours per ton requiresinstallation of a relatively expensive electronic measurement system.Therefore, it is preferred to infer the efficiency from other variables,preferably process temperatures and flow rates. Because of the complexinterdependencies of the variables, as well as the preferred use ofsurrogate variables instead of direct efficiency data, a non-linearneural network model may be employed, for example similar to the modelemployed by Bailey (1998). In this case, the model has an input layer,two hidden layers, and an output layer. The output layer typically hasone node for each controlled variable, while the input layer containsone node for each signal. The Bailey neural network includes five nodesin the first hidden layer and two nodes for each output node in thesecond hidden layer. Preferably, the sensor data is processed prior toinput into the neural network model. For example, linear processing ofsensor outputs, data normalization, statistical processing, etc. may beperformed to reduce noise, provide appropriate data sets, or to reducethe topological or computational complexity of the neural network. Faultdetection may also be integrated in the system, either by way of furtherelements of the neural network (or a separate neural network) or byanalysis of the sensor data by other means.

Feedback optimization control strategies are always applied to transientand dynamic situations. Evolutionary optimization or EVOP is a goodexample. Steady state optimization, on the other hand, is widely used oncomplex processes exhibiting long time constants and with disturbancevariables that change infrequently. Hybrid strategies are also employedin situations involving both long-term and short-term dynamics.Obviously the hybrid algorithms are more complex and require customtailoring for a truly effective implementation. Feedback control cansometimes be employed in certain situations to achieve optimal plantperformance. Evolutionary optimization, or EVOP, is one such techniqueusing feedback as the basis for its strategy. EVOP is an on-lineexperimenter. No extensive mathematical model is required, since smallperturbations of the independent control variable are made directly uponthe process itself. As in all optimizers, EVOP also requires anobjective function. EVOP does suffer certain limitations. The processmust be tolerant of some small changes in the major independentvariable. Secondly, it is necessary to apply EVOP or feedback control toperturb a single independent variable at a time. If a process isencountered, such that two independent variables are considered majorcontributors to the objective, then it may be possible to configure thecontroller to examine each one sequentially at alternate sampled-dataperiods. This latter approach is feasible only if the process dynamicsare rapid when compared with the frequency of expected changes in thedisturbance variables.

Multivariable processes in which there are numerous interactive effectsof independent variables upon the process performance can best beoptimized by the use of feedforward control. An adequate predictivemathematical model of the process is an absolute necessity. Note thatthe on-line control computer will evaluate the consequences of variablechanges using the model rather than perturbing the process itself.

To produce a viable optimization result, the mathematical model in afeedforward technique must be an accurate representation of the process.To ensure a one-to-one correspondence with the process, the model ispreferably updated just prior to each use. Model updating is aspecialized form of feedback in which model predictions are comparedwith the current plant operating status. Any variances noted are thenused to adjust certain key coefficients in the model to enforce therequired agreement.

In chillers, many of the relevant timeconstants are very long. Whilethis reduces short latency processing demands of a real time controller,it also makes corrections slow to implement, and poses the risk oferror, instability or oscillation if the timeconstants are erroneouslycomputed. Further, in order to provide a neural network with directtemporal control sensitivity, a large number of input nodes may berequired to represent the data trends. Preferably, temporal calculationsare therefore made by a linear computational method, with transformedtime-varying data input to the neural network. For example, first andsecond derivatives (or higher order, as may be appropriate) of sensordata may be calculated and fed to the network. Alternately oradditionally, the output of the neural network may be subjected toprocessing to generate appropriate process control signals. It is notedthat, for example, if the refrigerant charge in a chiller is varied, itis likely that critical timeconstants of the system will also vary.Thus, a model which presumes that the system has a set of invarianttimeconstants may produce errors. The control system thereforepreferably employs flexible models to account for the interrelation ofvariables.

Other potentially useful process parameters to measure include moisture,refrigerant breakdown products, lubricant breakdown products,non-condensable gasses, and other known impurities in the refrigerant.Likewise, there are also mechanical parameters which may haveoptimizable values, such as mineral deposits in the brine tubes (a smallamount of mineral deposits may increase turbulence and therefore reducea surface boundary layer), and air or water flow parameters for coolingthe condenser.

Typically, there are a set of process parameters which theoreticallyhave an optimum value of 0, while in practice, achieving this value isdifficult or impossible to obtain or maintain. This difficulty may beexpressed as a service cost or an energy cost, but in any case, thecontrol system may be set to allow theoretically suboptimal parameterreadings, which are practically acceptable and preferable toremediation. However, at some threshold, remediation is deemedefficient. The control system may therefore monitor these parameters andeither indicate an alarm, implement a control strategy, or otherwiseact. The threshold may, in fact, be adaptive or responsive to othersystem conditions; for example, a remediation process would preferablybe deferred during peak load periods if it adversely affects systemperformance.

Thus, it is seen that in some instances, as exemplified by oil levels inthe evaporator, an initial determination of system sensitivity to thesensed parameter is preferred, while in other instances, an adaptivecontrol algorithm is preferred.

In the case of autotuning processes, after the optimization calculationsare complete, the process variable, e.g., the oil content of theevaporator, may be restored to the optimal level. It is noted that theprocess variable may change over time, e.g., the oil level in theevaporator will increase, so it is desired to select an initialcondition which will provide the maximum effective efficiency betweenthe initial optimization and a subsequent maintenance to restore thesystem to efficient operation. Therefore, the optimization preferablydetermines an optimum operating zone, and the process variableestablished at the lower end of the zone after measurement. This lowerend may be zero, but need not be, and may vary for each system measured.

In this way, it is not necessary to continuously control the processvariable, and rather the implemented control algorithm may, for example,include a wide deadband and manual implementation of the controlprocess.

A monitor may be provided for the process variable, to determine whenreoptimization is necessary. During reoptimzation, it is not alwaysnecessary to conduct further efficiency measurements; rather, the priormeasurements may be used to redefine the desired operating regime.

Thus, after the measurements are taken to a limit (e.g., near zero oilor beyond the expected operating regime), the system is restored, ifnecessary, to achieve a desired initial efficiency, allowing for gradualvariations, e.g., accumulation of oil in the evaporator, while stillmaintaining appropriate operation for a suitable period.

An efficiency measurement, or surrogate measurement(s) (e.g., compressoramperage, thermodynamic parameters) may subsequently be employed todetermine when process variable, e.g., the oil level, has change oraccumulated to sufficient levels to require remediation. Alternately, adirect oil concentration measurement may be taken of the refrigerant inthe evaporator. In the case of refrigeration compressor oil, forexample, the monitor may be an optical sensor, such as disclosed in U.S.Pat. No. 5,694,210, expressly incorporated herein by reference.

It is also possible to provide a closed loop feedback device which seeksto maintain the process variable within a desired range. Thus, a directoil concentration gage, typically a refractometer, measures the oilcontent of the refrigerant. A setpoint control, proportional,differential, integral control, fuzzy logic control or the like is usedto control a bypass valve to a refrigerant distillation device, which istypically oversize, and operating well within its control limits. As theoil level increases to a level at which efficiency is impaired, therefrigerant is distilled to remove oil. The oil is, for example,returned to the compressor lubrication system, while the refrigerant isreturned to the compressor inlet. In this manner, closed loop feedbackcontrol may be employed to maintain the system at optimum efficiency. Itis noted that it is also possible to employ an active in-linedistillation process which does not bypass the evaporator. For example,the Zugibeast® system (Hudson Technologies, Inc.) may be employed,however, this is system typically larger and more complex thannecessary. U.S. Pat. No. 5,377,499, expressly incorporated herein byreference, thus provides a portable device for refrigerant reclamation.In this system, refrigerant may be purified on site, rather thanrequiring, in each instance, transporting of the refrigerant to arecycling facility. U.S. Pat. No. 5,709,091, expressly incorporatedherein by reference, also discloses a refrigerant recycling method andapparatus.

In the oil separating device, advantageously, the refrigerant is fedinto a fractional distillation chamber controlled to be at a temperaturebelow its boiling point, and therefore condenses into a bulk of liquidrefrigerant remaining within the vessel. Since the refrigerant is notemployed to remove heat from the water in the chiller, the amount ofcooling necessary to drop the refrigerant below its boiling point (atthe particular containment pressure) will approximately equal the heatabsorbed from the environment plus any inefficiencies in the system, arelatively modest amount in most cases. The distillation chamber has acontrolled temperature, and thus the more volatile fractions will tendto vaporize, leaving the bulk of refrigerant and less volatilefractions, including compressor oil. The vapors above the pool ofrefrigerant are relatively pure, while most contaminants remain in theliquid phase. As the contaminants accumulate in the liquid phase,portions may be drawn off and stored. Fresh refrigerant is fed toreplace the amounts withdrawn, to operate in a steady state. Therefrigerant vapors above the liquid phase are then compressed with acompressor, resulting in heating. The gasses are then cooled in a heatexchanger equilibrated with the bulk of liquid refrigerant in thedistillation chamber. This heat exchanger recondenses most of thecompressed gas therewithin, while the liquid refrigerant external isheated to compensate for the lost heat of vaporization of the purifiedrefrigerant. Where the temperature of the distillation chamber rises toohigh, the compressed refrigerant gasses bypass the heat exchanger, thuseffectively cooling the bulk of the liquid refrigerant due to the netloss of the heat of vaporization. The reliquified refrigerant is thensubjected to an auxiliary compressor which sheds heat added, forexample, by the primary compressor. The purified liquid refrigerant isthen available.

Thus, it is seen that the process may be manual or automated, continuousor batch.

The invention derives from a relatively new understanding that theoptimum oil level in the evaporator of a refrigeration system may varyby manufacturer, model and particular system, and that these variablesare significant in the efficiency of the process and may change overtime. The optimal oil level need not be zero, for example in fin tubeevaporators, the optimal oil level may be between 1-5%, at which the oilbubbles and forms a film on the tube surfaces, increasing heat transfercoefficient. On the other hand, so-called nucleation boiling heattransfer tubes have a substantially lower optimal oil concentration,typically less than about 1%.

Seeking to maintain a 0% oil concentration may itself be inefficient,since the oil removal process may require expenditure of energy andbypass of refrigerant, and given a low but continual level of leakage.Further, the oil level in the condenser may also impact systemefficiency, in a manner inconsistent with the changes in efficiency ofthe evaporator.

Thus, the present invention does not presume an optimum level of aparticular process variable parameter. Rather, a method according to theinvention explores the optimum value, and thereafter allows the systemto be set near the optimum. Likewise, the method permits periodic“tune-ups” of the system, rather than requiring continuous tightmaintenance of a control parameter, although the invention also providesa system and method for achieving continuous monitoring and/or control.

The refrigeration systems or chillers may be large industrial devices,for example 3500 ton devices which draw 4160V at 500 A max (2 MW).Therefore, even small changes in efficiency may produce substantialsavings in energy costs. Possibly more importantly, when efficiencydrops, it is possible that the chiller is unable to maintain the processparameter within the desired range. During extended operation, forexample, it is possible for the oil concentration in the evaporator toincrease above 10%, and the overall capacity of the system to drop below1500 tons. This can result in process deviations or failure, which mayrequire immediate or expensive remediation. Proper maintenance, toachieve a high optimum efficiency, may be extremely cost effective.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention will now be described with reference to the accompanyingdrawings, in which:

FIG. 1 is a schematic view of a known tube in shell heat exchangerevaporator;

FIG. 2 shows an end view of a tube plate, showing the radially symmetricarrangement of tubes of a tube bundle, each tube extending axially alongthe length of the heat exchanger evaporator;

FIG. 3 shows a schematic drawing of a partial distillation system forremoving oil from a refrigerant flow stream;

FIG. 4 shows a schematic of a chiller efficiency measurement system;

FIG. 5 shows a stylized representative efficiency graph with respect tochanges in evaporator oil concentration;

FIGS. 6A and 6B show, respectively, a schematic of a vapor compressioncycle and a temperature-entropy diagram; and

FIGS. 7A, 7B and 7C show, respectively, different block diagrams of acontrol according to the present invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

The foregoing and other objects, features and advantages of the presentinvention will become more readily apparent to those skilled in the artto which the invention pertains upon reference to the following detaileddescription of one of the best modes for carrying out the invention,when considered in conjunction with the accompanying drawing in whichpreferred embodiments of the invention are shown and described by way ofillustration, and not of limitation, wherein:

Example 1

As shown in FIGS. 1-2, a typical tube in shell heat exchanger 1 consistsof a set of parallel tubes 2 extending through a generally cylindricalshell 3. The tubes 2 are held in position with a tube plate 4, one ofwhich is provided at each end 5 of the tubes 2. The tube plate 4separates a first space 6, continuous with the interior of the tubes 7,from a second space 8, continuous with the exterior of the tubes 2.Typically, a domed flow distributor 9 is provided at each end of theshell 3, beyond the tube sheet 4, for distributing flow of the firstmedium from a conduit 10 through the tubes 2, and thence back to aconduit 11. In the case of volatile refrigerant, the system need not besymmetric, as the flow volumes and rates will differ at each side of thesystem. Not shown are optional baffles or other means for ensuringoptimized flow distribution patterns in the heat exchange tubes.

As shown in FIG. 3, a refrigerant cleansing system provides an inlet 112for receiving refrigerant from the condenser, a purification systememploying a controlled distillation process, and an outlet 150 forreturning purified refrigerant. This portion of the system is similar tothe system described in U.S. Pat. No. 5,377,499, expressly incorporatedherein by reference.

The compressor 100 compresses the refrigerant to a hot, dense gas. Thecondenser 107, sheds the heat in the gas, resulting from the compressor100. A small amount of compressor oil is carried with the hot gas to thecondenser 107, where it condenses, with the refrigerant, into a mixedliquid. The liquefied, cooled refrigerant (and oil) exits the condenserthrough line 108. Isolation valves 102, 109 are provided to selectivelyallow insertion of a partial distillation apparatus 105 within therefrigerant flow path. As shown, a fitting 14 receives the flow ofrefrigerant contents from the condenser 107 of the refrigeration system,though line 108. The refrigerant from the partial distillation apparatus105 is received by the evaporator 103 through the isolation valve 102.

The partial distillation apparatus 105 is capable of boilingcontaminated refrigerant in a distillation chamber 130 without the needfor external electrical heaters. Furthermore, no cooling water isrequired. The distillation temperature is controlled by throttling therefrigerant vapor. The distillation is accomplished by feedingcontaminated refrigerant, represented by directional arrow 110, throughan inlet 112 and a pressure regulating valve 114. The contaminatedrefrigerant flows into distillation chamber 116, to establish liquidlevel 118 of contaminated refrigerant liquid 120. A contaminated liquiddrain 121 is also provided, with valve 123. A high surface area conduit,such as a helical coil 122, is immersed beneath the level 118 ofcontaminated refrigerant liquid. Thermocouple 124 is placed at or nearthe center of coil 122 for measuring distillation temperature forpurposes of temperature control unit 126. In turn, the temperaturecontrol unit controls the position of three-way valve 128, so that thedistillation temperature will be set at a constant value atapproximately thirty degrees Fahrenheit (for R22 refrigerant).Temperature control valve 128 operates in a manner, with bypass conduit130, so that, as vapor is collected in the portion 132 of distillationchamber 116 above liquid level 118, it will feed through conduit 134 tocompressor 136. This creates a hot gas discharge at the output 138 ofcompressor 136, such that those hot gases feed through three-way valve128, under the control of temperature control 126. In those situations,where thermocouple 124 indicates a distillation temperature above thirtydegrees Fahrenheit, as an example, bypass conduit 130 will receive someflow of hot gases from compressor 136. Conversely, in those situationswhere thermocouple 124 indicates a temperature below thirty degreesFahrenheit, as an example, the flow of hot gases will proceed asindicated by arrow 140 into helical coil 122. When thermometer 124indicates certain values of temperature near thirty degrees Fahrenheit,hot gases from the compressor are allowed to flow partially along thebypass conduit and partially into the helical coil to maintain thethirty-degree temperature. For differing refrigerants or mixtures, thedesired boiling temperature may vary, and thus the temperature may becontrolled accordingly. Flow through bypass conduit 130 and from helicalcoil 122, in directions 142, 144, respectively, will pass throughauxiliary condenser 146 and pressure regulating valve 148 to produce adistilled refrigerant outlet indicated by directional arrow 150.Alternatively, condenser 146 is controlled by an additional temperaturecontrol unit, controlled by the condenser output temperature.

Thus, oil from the condenser 107 is removed before entering theevaporator 105. By running the system over time, oil accumulation in theevaporator 103 will drop, thus cleansing the system.

FIG. 4 shows an instrumented chiller system, allowing periodic or batchreoptimization, or allowing continuous closed loop feedback control ofoperating parameters. Compressor 100 is connected to a power meter 101,which accurately measures power consumption by measuring Volts and Ampsdrawn. The compressor 100 produces hot dense refrigerant vapor in line106, which is fed to condenser 107, where latent heat of vaporizationand the heat added by the compressor 100 is shed. The refrigerantcarries a small amount of compressor lubricant oil. The condenser 107 issubjected to measurements of temperature and pressure by temperaturegage 155 and pressure gage 156. The liquefied, cooled refrigerant,including a portion of mixed oil, if fed through line 108 to an optionalpartial distillation apparatus 105, and hence to evaporator 103. In theabsence of the partial distillation apparatus 105, the oil from thecondenser 107 accumulates in the evaporator 103. The evaporator 103 issubjected to measurements of refrigerant temperature and pressure bytemperature gage 155 and pressure gage 156. The chilled water in inletline 152 and outlet line 154 of the evaporator 103 are also subject totemperature and pressure measurement by temperature gage 155 andpressure gage 156. The evaporated refrigerant from the evaporator 103returns to the compressor through line 104.

The power meter 101, temperature gage 155 and pressure gage 156 eachprovide data to a data acquisition system 157, which produces output 158representative of an efficiency of the chiller, in, for example,BTU/kWH. An oil sensor 159 provides a continuous measurement of oilconcentration in the evaporator 103, and may be used to control thepartial distillation apparatus 105 or determine the need forintermittent reoptimization, based on an optimum operating regime. Thepower meter 101 or the data acquisition system 157 may provide surrogatemeasurements to estimate oil level in the evaporator or otherwise a needfor oil removal.

As shown in FIG. 5, the efficiency of the chiller varies with the oilconcentration in the evaporator 103. Line 162 shows a non-monotonicrelationship. After the relationship is determined by plotting theefficiency with respect to oil concentration, an operating regime maythereafter be defined. While typically, after oil is removed from theevaporator 103, it is not voluntarily replenished, a lower limit 160 ofthe operating regime defines, in a subsequent removal operation, aboundary beyond which it is not useful to extend. Complete oil removalis not only costly and directly inefficient, it may also result inreduced system efficiency. Likewise, when the oil level exceeds an upperboundary 161 of the operating regime, system efficiency drops and it iscost effective to service the chiller to restore optimum operation.Therefore, in a close loop feedback system, the distance between thelower boundary 160 and upper boundary will be much narrower than in aperiodic maintenance system. The oil separator (e.g., partialdistillation apparatus 105 or other type system) in a closed loopfeedback system is itself typically less efficient than a larger systemtypically employed during periodic maintenance, so there are advantagesto each type of arrangement.

Example 2

FIG. 7A shows a block diagram of a first embodiment of a control systemaccording to the present invention. In this system, refrigerant chargeis controlled using an adaptive control 200, with the control receivingrefrigerant charge level 216 (from a level transmitter, e.g., HenryValve Co., Melrose Park Ill. LCA series Liquid Level Column with E-9400series Liquid Level Switches, digital output, or K-Tek MagnetostrictiveLevel Transmitters AT200 or AT600, analog output), optionally systempower consumption (kWatt-hours), as well as thermodynamic parameters,including condenser and evaporator water temperature in and out,condenser and evaporator water flow rates and pressure, in and out,compressor RPM, suction and discharge pressure and temperature, andambient pressure and temperature, all through a data acquisition systemfor sensor inputs 201. These variables are fed into the adaptive control200 employing a nonlinear model of the system, based on neural network203 technology. The variables are preprocessed to produce a set ofderived variables from the input set, as well as to represent temporalparameters based on prior data sets. The neural network 203 evaluatesthe input data set periodically, for example every 30 seconds, andproduces an output control signal 209 or set of signals. After theproposed control is implemented, the actual response is compared with apredicted response based on the internal model defined by the neuralnetwork 203 by an adaptive control update subsystem 204, and the neuralnetwork is updated 205 to reflect or take into account the “error”. Afurther output 206 of the system, from a diagnostic portion 205, whichmay be integrated with the neural network or separate, indicates alikely error in either the sensors and network itself, or the plantbeing controlled.

The controlled variable is, for example, the refrigerant charge in thesystem. In order to remove refrigerant, liquid refrigerant from theevaporator 211 is transferred to a storage vessel 212 through a valve210. In order to add refrigerant, gaseous refrigerant may be returned tothe compressor 214 suction, controlled by valve 215, or liquidrefrigerant pumped to the evaporator 211. Refrigerant in the storagevessel 212 may be subjected to analysis and purification.

Example 3

A second embodiment of the control system employs feedfowardoptimization control strategies, as shown in FIG. 7B. FIG. 7B shows asignal-flow block diagram of a computer-based feedforward optimizingcontrol system. Process variables 220 are measured, checked forreliability, filtered, averaged, and stored in the computer database222. A regulatory system 223 is provided as a front line control to keepthe process variables 220 at a prescribed and desired slate of values.The conditioned set of measured variables are compared in the regulatorysystem 223 with the desired set points from operator 224A andoptimization routine 224B. Errors detected are then used to generatecontrol actions that are then transmitted as outputs 225 to finalcontrol elements in the process 221. Set points for the regulatorysystem 223 are derived either from operator input 224A or from outputsof the optimization routine 224B. Note that the optimizer 226 operatesdirectly upon the model 227 in arriving at its optimal set-point slate224B. Also note that the model 227 is updated by means of a specialroutine 228 just prior to use by the optimizer 227. The feedback updatefeature ensures adequate mathematical process description in spite ofminor instrumentation errors and, in addition, will compensate fordiscrepancies arising from simplifying assumptions incorporated in themodel 227. In this case, the controlled variable may be, for example,compressor speed, alone or in addition to refrigerant charge level.

The input variables are, in this case, similar to those in Example 2,including refrigerant charge level, optionally system power consumption(kWatt-hours), as well as thermodynamic parameters, including condenserand evaporator water temperature in and out, condenser and evaporatorwater flow rates and pressure, in and out, compressor RPM, suction anddischarge pressure and temperature, and ambient pressure andtemperature.

Example 4

As shown in FIG. 7C, a control system 230 is provided which controlsrefrigerant charge level 231, compressor speed 232, and refrigerant oilconcentration 233 in evaporator. Instead of providing a single complexmodel of the system, a number of simplified relationships are providedin a database 234, which segment the operational space of the systeminto a number of regions or planes based on sensor inputs. Thesensitivity of the control system 230 to variations in inputs 235 isadaptively determined by the control during operation, in order tooptimize energy efficiency.

Data is also stored in the database 234 as to the filling density of theoperational space; when the set of input parameters identifies a wellpopulated region of the operational space, a rapid transition iseffected to achieve the calculated most efficient output conditions. Onthe other hand, if the region of the operational space is poorlypopulated, the control 230 provides a slow, searching alteration of theoutputs seeking to explore the operational space to determine theoptimal output set. This searching procedure also serves to populate thespace, so that the control 230 will avoid the naïve strategy after a fewencounters.

In addition, for each region of the operational space, a statisticalvariability is determined. If the statistical variability is low, thenthe model for the region is deemed accurate, and continual searching ofthe local region is reduced. On the other hand, if the variability ishigh, the control 230 analyzes the input data set to determine acorrelation between any available input 235 and the system efficiency,seeking to improve the model for that region stored in the database 234.This correlation may be detected by searching the region throughsensitivity testing of the input set with respect to changes in one ormore of the outputs 231, 232, 233. For each region, preferably a linearmodel is constructed relating the set of input variables and the optimaloutput variables. Alternately, a relatively simple non-linear network,such as a neural network, may be employed.

The operational regions, for example, segment the operational space intoregions separated by 5% of refrigerant charge level, from −40% to +20%of design, oil content of evaporator by 0.5% from 0% to 10%, andcompressor speed, from minimum to maximum in 10-100 increments. It isalso possible to provide non-uniformly spaced regions, or evenadaptively sized regions based on the sensitivity of the outputs toinput variations at respective portions of the input space.

The control system also provides a set of special modes for systemstartup and shutdown. These are distinct from the normal operationalmodes, in that energy efficiency is not generally a primaryconsideration during these transitions, and because other control issuesmay be considered important. These modes also provide options forcontrol system initialization and fail-safe operation.

It is noted that, since the required update time for the system isrelatively long, the neural network calculations may be implementedserially on a general purpose computer, e.g., an Intel Pentium IIIprocessor running Windows NT or a real time operating system, andtherefore specialized hardware is typically not necessary.

It is preferred that the control system provide a diagnostic output 236which “explains” the actions of the control, for example identifying,for any given control decision, the sensor inputs which had the greatestinfluence on the output state. In neural network systems, however, it isoften not possible to completely rationalize an output. Further, wherethe system detects an abnormal state, either in the plant beingcontrolled or the controller itself, it is preferred that information becommunicated to an operator or service engineer. This may be by way of astored log, visual or audible indicators, telephone or Internettelecommunications, control network or local area networkcommunications, radio frequency communication, or the like. In manyinstances, where a serious condition is detected and where the plantcannot be fully deactivated, it is preferable to provide a “failsafe”operational mode until maintenance may be performed.

The foregoing description of the preferred embodiment of the inventionhas been presented for purposes of illustration and description and isnot intended to be exhaustive or to limit the invention to the preciseforms disclosed, since many modifications and variations are possible inlight of the above teaching. Some modifications have been described inthe specifications, and others may occur to those skilled in the art towhich the invention pertains.

What is claimed is:
 1. A refrigeration system controller, comprising: asensor input port configured to receive sensor signals selectivelydependent on and representing a thermodynamic operating state of arefrigeration system and being sufficient to determine a thermodynamicoperating efficiency of the refrigeration system; a computational modelof operation of a refrigeration system, the computational modelcomprising parameters derived from the sensor input and a plurality ofcontrol signals for controlling operation of the refrigeration systemover time; at least one automated processor configured to produce theplurality of control signals, based on at least the computational modelof the refrigeration system, the thermodynamic operating state of therefrigeration system, and the sensor signals; wherein the at least oneautomated processor is configured to: determine changes in thethermodynamic operating efficiency of the refrigeration system withrespect to the thermodynamic operating state over time, and in at leastone mode, produce the control signals to achieve a predicted increasedoptimum net efficiency of the refrigeration system at the thermodynamicoperating state over time, by initial alteration of the thermodynamicoperating state to a transient thermodynamic operating state lessefficient than a preceding thermodynamic operating state, and subsequentalteration of the refrigeration system to a persistent thermodynamicoperating state more efficient than each of the transient thermodynamicoperating state and the preceding thermodynamic operating state; and acontrol output port configured to communicate the plurality of controlsignals for controlling operation of the refrigeration system over time.2. The refrigeration system controller according to claim 1, wherein thecomputational model is an adaptive computational model.
 3. Therefrigeration system controller according to claim 1, wherein thecomputational model comprises an artificial neural network.
 4. Therefrigeration system controller according to claim 1, wherein the atleast one automated processor is further configured to determine avarying refrigeration system response timeconstant, and to control therefrigeration system over time selectively in dependence on the varyingtimeconstant, to damp an oscillation of the thermodynamic operatingstate of the refrigeration system.
 5. The refrigeration systemcontroller according to claim 1, wherein the at least one automatedprocessor is further configured to predict a need for refrigerationsystem maintenance while the refrigeration system remains operational,further comprising a maintenance signal output port configured tocommunicate a maintenance signal generated by the at least one automatedprocessor which is selectively dependent on at least the predicted needfor refrigeration system maintenance.
 6. The refrigeration systemcontroller according to claim 5, wherein the at least one automatedprocessor is further configured to predict the need for refrigerationsystem maintenance based on adaptive criteria which differ dependent ona history of sensor signals and the plurality of control signals.
 7. Therefrigeration system controller according to claim 5, further comprisingan interface port configured to communicate with a local area network,wherein the at least one automated processor is further configured tocommunicate the predicted the need for refrigeration system maintenanceover the local area network through the interface port.
 8. Therefrigeration system controller according to claim 1, further comprisingan Internet communication interface port, wherein the at least oneautomated processor is further configured to communicate at least one ofthe sensor signals and the plurality of control signals through theInternet communication interface port.
 9. The refrigeration systemcontroller according to claim 5, wherein the at least one automatedprocessor is further configured to determine a probability ofrefrigeration system malfunction and to produce a probable malfunctionsignal selectively dependent on the determined probability ofrefrigeration system malfunction.
 10. The refrigeration systemcontroller according to claim 1, wherein the at least one automatedprocessor is configured to determine a cost efficiency of operation ofthe refrigeration system.
 11. The refrigeration system controlleraccording to claim 1, wherein the optimum net efficiency over time isbased on at least a predicted service cost.
 12. The refrigeration systemcontroller according to claim 1, wherein the at least one automatedprocessor is configured to concurrently produce at least two distinctcontrol signals, each distinct control signal being adapted toindependently control different physical elements of the refrigerationsystem.
 13. The refrigeration system controller according to claim 1,wherein the at least one automated processor is configured to determinethe optimum net efficiency based on at least a value attributed toremoving heat by the refrigeration system.
 14. A method of controlling arefrigeration system, comprising: receiving sensor signals selectivelydependent on a thermodynamic operating state of a refrigeration system;a computational model of a refrigeration system for processing by atleast one automated processor, comprising computational model parametersderived from the sensor signals and a plurality of control signals forthe refrigeration system over time; producing the plurality of controlsignals with at least one automated processor, based on at least thecomputational model, the thermodynamic operating state of therefrigeration system, and the sensor signals; determining changes in anefficiency of the refrigeration system over time, comprising changes ina thermodynamic efficiency of the refrigeration system; producing thecontrol signals based on a predicted optimum net efficiency over time,by transiently altering the thermodynamic operating state of therefrigeration system to a less efficient thermodynamic operating statethan an efficiency of a prior thermodynamic operating state, andsubsequently persistently altering the thermodynamic operating state ofthe refrigeration system to a more efficient thermodynamic operatingstate than either the less efficient thermodynamic operating state orthe prior thermodynamic operating state.
 15. The method according toclaim 14, wherein the computational model is an adaptive computationalmodel.
 16. The method according to claim 14, wherein the computationalmodel comprises an artificial neural network.
 17. The method accordingto claim 14, further comprising determining a time-varying refrigerationsystem response timeconstant, and controlling the refrigeration systemselectively in dependence on the time-varying timeconstant.
 18. Themethod according to claim 14, further comprising communicating at leastone of the sensor signals and the control signals over the Internet. 19.The method according to claim 14, further comprising determining a costefficiency of operation of the refrigeration system.
 20. An automatedcontroller for controlling a refrigeration system, comprising: a sensorinput port configured to receive sensor signals selectively dependent ona thermodynamic operating state of a refrigeration system; a dynamiccomputational model of the refrigeration system, comprisingcomputational model parameters derived from the sensor input and aplurality of control signals for the refrigeration system over time; atleast one automated processor configured to produce the plurality ofcontrol signals, based on at least the computational model, thethermodynamic operating state, and the sensor signals; wherein the atleast one automated processor is configured to produce the controlsignals to alter the thermodynamic operating state of the refrigerationsystem to a lower efficiency operating state that a preexistingthermodynamic operating state, before assuming a higher efficiencythermodynamic operating state, the control signals being generated tooptimize a predicted net efficiency of the refrigeration system overtime; and a control output port configured to present the plurality ofcontrol signals.